基于關(guān)聯(lián)向量機的氣體傳感器陣列信號處理
發(fā)布時間：2018-10-23 15:22
【摘要】：氣體傳感器陣列由多個傳感器構(gòu)成,利用氣體傳感器特有的“交叉敏”特點,每個傳感器對要測的氣味信息都有著不同的敏感度、選擇性和反復(fù)性。在傳感器陣列多維空間中形成響應(yīng)模式,陣列所確定的多維空間也包含了更多的信息。但是由于現(xiàn)在的氣味成分信息、越來越復(fù)雜,僅靠傳感器陣列已經(jīng)不能全面和準確的分析氣味信息,所以本文引入關(guān)聯(lián)向量機(Relevance Vector Machine, RVM)對氣體傳感器陣列信號進行處理。RVM是一種新的機器學(xué)習(xí)方法,用于氣體傳感器陣列信號處理,具有良好的泛化性能、概率式預(yù)測等特點。本文選取中草藥白薇和五種常見的稻米為例。采用傳感器陣列、模式識別技術(shù)相結(jié)合的方法對中草藥白薇貨架期進行判定和檢測稻米的質(zhì)量。在進行實驗樣本分類之前,本文采用主成分分析法對數(shù)據(jù)進行預(yù)處理,不僅降低了計算的復(fù)雜程度,同時也提高了分類效率。為了突出本文RVM分類的有效性和可行性,將其和支持向量機(Support Vector Machine, SVM)、神經(jīng)網(wǎng)絡(luò)等方法進行對比。本論文的主要研究工作如下：(1)傳感器陣列信號的采集。本文選擇5種稻米和中草藥白薇作為研究對象。使用電子鼻進行實驗,采集實驗樣本信息。用主成分分析法對實驗樣本進行特征提取,并對初始特征向量進行主成分分析,保留樣本的主要成分信息,達到降維的目的,減少計算量,提高分類效率。(2)采用RVM對實驗樣本進行分類和貨架期判定。采用實驗法確定分類模型的核函數(shù)及核參數(shù),并比較不同核函數(shù)和核參數(shù)對分類識別精度的影響,從而確定最優(yōu)的分類模型。在二分類實驗中,實驗結(jié)果表明采用高斯(Gauss)和三次多項式(Ploy3)核函數(shù),分類精度較高,相關(guān)向量數(shù)較少,分類所需時間相對較短。選擇Poly3核函數(shù)時運行時間最短,便于在線實時檢測。再將RVM二分類推廣到多分類,比較不同核函數(shù)和核參數(shù)時,分類模型的準確率。(3)比較不同算法的分類精度。對RVM、SVM和神經(jīng)網(wǎng)絡(luò)方法進行分類比較。實驗結(jié)果驗證了本文算法可以有效的克服SVM存在的測試時間長、支持向量個數(shù)過多等問題。從測試時間來看,在BP、RBF和SVM都選擇高斯核函數(shù)時,RVM的測試時間比BP、RBF、SVM都要短很多。RVM不需要改動其他參數(shù),核函數(shù)也不用符合Mercer條件。通過實驗結(jié)果表明,將RVM的分類技術(shù)引入到稻米分類和貨架期判定,可以保證較高的分類精度,獲得的模型更加稀疏、測試時間也縮短。與其他方法的對比結(jié)果,驗證了本文RVM模型用于分類識別的有效性和可行性,同時也適用于其他樣本的分類檢測。
[Abstract]:The gas sensor array is composed of multiple sensors. Each sensor has different sensitivity, selectivity and repetition to the odour information to be measured by using the characteristic of "cross sensitivity" of gas sensor. The response mode is formed in the sensor array multidimensional space, and the multidimensional space determined by the array also contains more information. However, due to the increasing complexity of odour information, it is not possible to analyze odor information comprehensively and accurately by using sensor arrays alone. In this paper, we introduce the correlation vector machine (Relevance Vector Machine, RVM) to process the gas sensor array signal. RVM is a new machine learning method, which is used in the gas sensor array signal processing, and has good generalization performance and probabilistic prediction. In this paper, Bai Wei and five common rice varieties are selected as examples. Using sensor array and pattern recognition technology, the shelf life of Chinese herbal medicine Bai Wei was determined and the quality of rice was tested. Before the experiment sample classification, the principal component analysis (PCA) is used to preprocess the data, which not only reduces the complexity of calculation, but also improves the classification efficiency. In order to highlight the validity and feasibility of RVM classification in this paper, it is compared with support vector machine (SVM) (Support Vector Machine, SVM), neural network. The main work of this thesis is as follows: (1) acquisition of sensor array signal. In this paper, five kinds of rice and Chinese herbal medicine Bai Wei were selected as the research object. The electronic nose is used to carry out the experiment, and the information of the experiment sample is collected. The principal component analysis (PCA) is used to extract the features of the experimental samples, and the initial eigenvector is analyzed to preserve the information of the main components of the samples, so as to achieve the purpose of reducing the dimension and reducing the amount of calculation. Improve the classification efficiency. (2) RVM was used to classify the experimental samples and determine the shelf life. The kernel function and kernel parameters of the classification model are determined by the experimental method, and the effects of different kernel functions and kernel parameters on the classification recognition accuracy are compared to determine the optimal classification model. In the two-classification experiment, the experimental results show that Gao Si (Gauss) and cubic polynomial (Ploy3) kernel functions have higher classification accuracy, fewer correlation vectors and shorter classification time. The Poly3 kernel function has the shortest running time and is easy to detect in real time. Then the RVM two-classification is extended to multi-classification to compare the accuracy of the classification models with different kernel functions and kernel parameters. (3) the classification accuracy of different algorithms is compared. The classification of RVM,SVM and neural network is compared. Experimental results show that the proposed algorithm can effectively overcome the problems of long test time and excessive number of support vectors in SVM. From the point of view of test time, when BP,RBF and SVM choose Gao Si kernel function, the test time of RVM is much shorter than that of BP,RBF,SVM. RVM does not need to change other parameters and the kernel function does not have to meet the Mercer condition. The experimental results show that the introduction of RVM classification technology to rice classification and shelf life determination can ensure higher classification accuracy, the obtained model is more sparse, and the test time is shortened. Compared with other methods, the effectiveness and feasibility of the proposed RVM model for classification and recognition are verified. At the same time, it is also applicable to the classification and detection of other samples.
【學(xué)位授予單位】：浙江師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP212;TN911.7
本文編號：2289644
[Abstract]:The gas sensor array is composed of multiple sensors. Each sensor has different sensitivity, selectivity and repetition to the odour information to be measured by using the characteristic of "cross sensitivity" of gas sensor. The response mode is formed in the sensor array multidimensional space, and the multidimensional space determined by the array also contains more information. However, due to the increasing complexity of odour information, it is not possible to analyze odor information comprehensively and accurately by using sensor arrays alone. In this paper, we introduce the correlation vector machine (Relevance Vector Machine, RVM) to process the gas sensor array signal. RVM is a new machine learning method, which is used in the gas sensor array signal processing, and has good generalization performance and probabilistic prediction. In this paper, Bai Wei and five common rice varieties are selected as examples. Using sensor array and pattern recognition technology, the shelf life of Chinese herbal medicine Bai Wei was determined and the quality of rice was tested. Before the experiment sample classification, the principal component analysis (PCA) is used to preprocess the data, which not only reduces the complexity of calculation, but also improves the classification efficiency. In order to highlight the validity and feasibility of RVM classification in this paper, it is compared with support vector machine (SVM) (Support Vector Machine, SVM), neural network. The main work of this thesis is as follows: (1) acquisition of sensor array signal. In this paper, five kinds of rice and Chinese herbal medicine Bai Wei were selected as the research object. The electronic nose is used to carry out the experiment, and the information of the experiment sample is collected. The principal component analysis (PCA) is used to extract the features of the experimental samples, and the initial eigenvector is analyzed to preserve the information of the main components of the samples, so as to achieve the purpose of reducing the dimension and reducing the amount of calculation. Improve the classification efficiency. (2) RVM was used to classify the experimental samples and determine the shelf life. The kernel function and kernel parameters of the classification model are determined by the experimental method, and the effects of different kernel functions and kernel parameters on the classification recognition accuracy are compared to determine the optimal classification model. In the two-classification experiment, the experimental results show that Gao Si (Gauss) and cubic polynomial (Ploy3) kernel functions have higher classification accuracy, fewer correlation vectors and shorter classification time. The Poly3 kernel function has the shortest running time and is easy to detect in real time. Then the RVM two-classification is extended to multi-classification to compare the accuracy of the classification models with different kernel functions and kernel parameters. (3) the classification accuracy of different algorithms is compared. The classification of RVM,SVM and neural network is compared. Experimental results show that the proposed algorithm can effectively overcome the problems of long test time and excessive number of support vectors in SVM. From the point of view of test time, when BP,RBF and SVM choose Gao Si kernel function, the test time of RVM is much shorter than that of BP,RBF,SVM. RVM does not need to change other parameters and the kernel function does not have to meet the Mercer condition. The experimental results show that the introduction of RVM classification technology to rice classification and shelf life determination can ensure higher classification accuracy, the obtained model is more sparse, and the test time is shortened. Compared with other methods, the effectiveness and feasibility of the proposed RVM model for classification and recognition are verified. At the same time, it is also applicable to the classification and detection of other samples.
【學(xué)位授予單位】：浙江師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP212;TN911.7
【參考文獻】
相關(guān)期刊論文 前1條
1 范伊紅;李敏;張元;;相關(guān)向量機在車型識別中的應(yīng)用研究[J];計算機工程與設(shè)計;2008年06期
，本文編號：2289644
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